Hail Climatology for Canada: An Update
By David Etkin, York University
February 2018

Hail Climatology for Canada: An Update
By David Etkin, York University
February 2018
ICLR research paper series â&#x20AC;&#x201C; number 59

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Acknowledgements: The author would like to acknowledge Farah Tasneem for her expert
assistance in mapping the hail frequencies, and Dr. Jenaro Nosedal for processing of the raw
Environment Canada data. I wish to thank the Institute for Catastrophic Loss Reduction for
funding this study, and Glenn McGillivray in particular for his support, and understanding that
research projects do not always adhere to predetermined timelines.

Hail is a significant hazard in parts of Canada, causing damage to infrastructure and crops. It is of
particular concern to the insurance industry, for which it is an important risk. Risk analysis begins with
an understanding of hazard, and for this reason it is important to have an updated climatology of
hail frequency in Canada. A previous national climatology was based upon data from 1977 to 1993
(Etkin & Brun, 1999), and therefore it is prudent to examine Canada’s hail climatology based upon a
longer time series that includes more recent data.
Hail events have been recorded across Canada, but the storms resulting in the greatest damage to
property and crops are most common in southern Alberta. Over the past 25 years, there have been
several severe thunderstorms with hail in Alberta resulting in hundreds of millions of dollars in
insurance damage claims. Research produced for ICLR by AIR Worldwide suggests that a low
probability/worst case storm event could result in insurance damage claims of up to $13.5 billion
from a single event.
In the 1990s and early 2000s, ICLR conducted a number of studies focused on understanding the risk
of hail damage in Canada. The hail research needs of insurance companies was acute before ICLR
was established when Canada’s most costly hailstorm struck Calgary in 1991. In particular, ICLR
published an earlier hail climatology: 1977-1993 and conducted several workshops where hail was
considered as part of a broader discussion of convective storm-related losses. Institute members
also contributed to an industry discussion that lead to the creation of the Alberta Severe Weather
Management Society.
Fortunately, there were few large hail damage events in Canada between 1991 and 2008. Indeed,
there was a period of almost ten years when the Institute received virtually no requests from member
companies to study the peril. The industry directed ICLR to focus its research on other hazards,
including the alarming increased in water damage. Indeed, hail research was not included in the
Institute’s last five-year plan.
However, hail damage claims have ramped up in Canada in recent years. Just three wind/water/hail
events in Alberta (2010, 2012 and 2014) totaled more than $1.66 billion in insured losses. As a result,
in 2015 Canadian property and casualty insurers – through ICLR’s Insurance Advisory Committee –
formally asked the Institute to investigate the peril and suggest actions insurers can take to mitigate
future hail losses in the country.
National hail climatologies (e.g. the number of hail days per year in Canada) serve as a foundation for
such hail risk analyses. Although national hail climatologies cannot be used to determine hailstorm
severity or to infer damage, they are used to help identify vulnerable regions, and thus areas where
mitigation efforts should be concentrated.

1

2. Literature review

Hail is a natural hazard that causes significant damage to Canadian society (particularly agriculture,
infrastructure and automobiles). For example, in 2014 a hailstorm in Alberta cost the insurance
industry $524 million (IBC, 2016). Insured losses to Canadian crops average about $200 million/year
(Air Worldwide, 2016).
Insured Canadian losses to property (not including agricultural losses) due to 24 hail-only1 events
from 1986-2015 total $1.9 billion in 2015 CDN dollars (IBC, 2016). The single most expensive event
($524 million in Calgary, Alberta in 1991) accounts for 27% of the total hail-only costs (Figure 1).
Three events in 2010, 2012 and 2014, combined with wind and water, resulted in a further $1.66
billion in insured damage; most of those losses were hail-related (McGillivray, 2017). Two additional
combined events in 2015 cost $480 million and twelve other damaging events between 2008 and
2013 occurred but do not have loss estimates associated with them.
A rank ordered 2 plot of catastrophic hail-only loss data from the IBC report (Figure 1) suggests that
losses due to hail may well follow a fat-tailed distribution such as a power law (Clauset et al., 2009).
This means that very rare extreme events account for a relatively large fraction of total impacts.
This also is evident within the agricultural industry, where “85% of total crop losses in a year can be
caused by only 10 –15% of the hailstorms” (Air Worldwide, 2016). For fat-tailed distributions
empirical calculations of mean annualized loss may be poor predictors of future risk, and this has
important implications for risk analyses. For example, Cook et al. (2014) notes that in such
distributions “yearly losses can be hopelessly volatile and, as such, historical averages are not good
predictors of future losses.” Future research might address what statistical distribution best fits hail
probability data, and how the fatness of the tail might change due to climate change and shifts in
exposure and urban density.
Of concern is that hail may become a greater issue in the future due to climate change potentially
increasing the frequency of severe thunderstorms and urban development increasing exposure.
A number of studies have highlighted increasing risk related to convective storms. For example,
Foote et al. (2005) note that insurance claims due to hailstorms in the urban U.S. escalated from
1995-2005. Changnon (2008) found that in the U.S. “The nation’s top ten loss events during
1950–2006 reveal a notable temporal increase with most losses in the 1992–2006 period. Causes for
these increases could be an increasing frequency of very unstable atmospheric conditions leading to
bigger, longer lasting storms, and/or a greatly expanded urban society that has become increasingly
vulnerable to hailstorms.” Single events can be very damaging; hailstorms on April 13-14, 2006
resulted in properly losses of $1.8 billion in the U.S.
Future trends are unclear in terms of severe convective storms (which require both an unstable
atmosphere and significant wind shear, to develop) producing hail. Brooks (2013) notes the
following: “Climate model simulations suggest that CAPE (convective available potential energy)
will increase in the future and the wind shear will decrease. Detailed analysis has suggested that
the CAPE change will lead to more frequent environments favorable for severe thunderstorms, but
the strong dependence on shear for tornadoes, particularly the strongest ones, and hail means that
the interpretation of how individual hazards will change is open to question.”

1

Events listed as hail/wind/flood are not included.

2

This means that the most expensive event is ranked #1, the second most expensive event as rank #2, and so on.

2

Figure 1: Insured property hail damage in Canada (1981-2015). Source: IBC (2016).
Rank 1 is the most expensive event (Calgary hailstorm of 1991) and dominates
the dataset, comprising 27% of the total cost of the 24 events.

Insured hail damage rank ordered
600,000

Cost $ 2015 (000)

500,000
400,000

300,000

200,000
100,000
0
0

5

10

15

20

25

Rank

Year-to-year variability can be large when it comes to hail occurrence, which is dependent upon
other large scale climate events such as the ENSO oscillation (Mayes et al., 2007). Nevertheless, there
may be trends of hail occurrence embedded in the data. With respect to tornado occurrences in
Ontario, Cao et al. (2011) found an increasing trend. Tornadoes, like hail, originate from severe
thunderstorms, and an increase in tornado frequency could very possibly be accompanied by an
increase in hail frequency. With respect to hail risk the occurrence of severe hail is of particular
interest, though the Environment Canada data set used in this study does not capture it.
Cao (2008) using data from the Ontario Storm Prediction Centre for the period 1979-2002 found a
significant increase in severe hail (defined as hail with diameter of at least 2 cm) frequency and
variability in Ontario, associated with warmer air temperatures (Figure 2).
In the U.S., Tippett et al. (2015) â&#x20AC;&#x153;found increasing trends in the frequency of the most extreme
U.S. environments over the last three decadesâ&#x20AC;?, which is in line with climate change projections.
Changnon (2009) found that the 10 greatest hail losses suggest increases in frequency from
1990-2009. As of July 9, 2017, the U.S. has had 9 disasters exceeding a billion dollars, including
hailstorms in Minnesota and Colorado that total $4.7 billion (Dolce, 2017).

There have been a number of regional and global hail climatology assessments, such as one based
upon the Alberta Hail Project, which ran from 1956-1985 (Table 1). Data for Table 1 is based upon
surveys of about 20,000 farmers, thus creating a high density observing network (Brimelow and
Reuter, 2002). It is interesting to note that Alberta has also been the focus of a cloud seeding project,
which may have affected the frequency of severe hail occurrences, but is unlikely to have affected
hail frequency overall. Gilbert et al. (2016) found that in one case study, seeded hail storms had a
smaller severe area than non-seeded storms.

Table 1: An Analysis of Hail Days in Alberta based on 29 years of data from the
Alberta Hail Project (Smith et al., 1998). Severe hail episodes (mean = 4.2 days) last
nearly twice as long as non-severe ones (mean = 2.1 days).
Event

Mean Hail Days for Central Alberta

Hail days

52.4 (57%)

Severe hail days (at least one day of maximum hail
size >33mm)

5.2 (6%)

Non-severe hail days

47.2 (51%)

No-hail days

39.6 (43%)

In another study that did a global analysis (Cecil and Blankenship, 2012), severe hail storm
frequencies were estimated for Canada (Figure 3).

A man hitches up a tow rope to pull a car from the 21 cm of hail it got stuck in on
Hwy 2A just south of Red Deer, Alberta, Thursday August 10, 2006. A fierce storm swept
through Central Alberta that evening, dumping hail, rain and forcing traffic to stop on
Queen Elizabeth II Highway.

[CP PHOTO/Red Deer Advocate â&#x20AC;&#x201C; Randy Fiedler]

5

3. Data & methodology

Hail days data (climate element #16) was obtained from the Digital Archive of Canadian
Climatological Data, Environment Canada from all hail observing stations (Figure 4). For each station,
monthly days-with-hail were calculated where the number of missing observations were less than
4 days in any month. This represents 96.7% of the records. Monthly hail days were adjusted for
missing data by multiplying the unadjusted hail-day observation by the factor [1+ (number of missing
days) Ăˇ (number of days in the month)].
The number of years of hail observations for the stations varies (Figure 5), and in order to balance
data quantity with estimation errors due to climate variability, only stations with at least 19 years of
hail observations were used for the analysis. Of the 8,737 stations, this left 3,600 that were used in
this analysis.
Figure 6 shows the number of stations observing hail from 1955 to 2015. As noted in Etkin & Brun
(1999), hail observations at Environment Canada weather and climate stations were not widespread
until 1977. After 1993 the number of hail observing stations began to decline and after 2005 the
number of stations reporting hail dropped precipitately; after 2007 the number was trivial. The
current hail climatology is therefore based upon observations from 1977 to 2007.
Figure 4: Hail observing stations in Canada

Locations of hail observing stations

For each station for the months of May to September, a monthly hail frequency was calculated by
summing the number of hail-day observations over the period of record and dividing by the number
of months. This data was then imported into SURFER 3, which converted the station data into gridded
data. For the regional maps gridded data was calculated using a search radius of 2 degrees latitude.
Grid sizes of 1 degree were used, and a Kriging algorithm used to create contours. This data was

Number of years of record
then imported into ArcGIS. A spline smoothing function in ArcGIS 4 was applied to smooth the
contours. A close-up for parts of western Canada was mapped using a 1 degree latitude search
radius and a grid size of 0.5 degrees. Total warm season frequencies were calculated by summing the
gridded data for the 5 months.
Figure 6: Number of stations observing hail (May-September). Months with more
than 3 missing observations and stations with less than 19 years of data are excluded.
15,000

At this point it is important to state a caveat. The mapping in this project is based upon an objective
analysis of station data, and the contours are not adjusted to incorporate the effects of topography,
which can be significant. Where stations are far apart and their density does not capture
topographic features, contours may well differ from actual hail patterns. Additionally, edge effects
can make the contours less reliable at borders or where data ends.
The hail frequency maps in Appendix A (in units of number of days/month with hail) show similar
patterns to previous analyses. The primary features are (Table 2):
Table 2: Summary of Hail Frequency Patterns by Month

5

Month

Western Canada

Central Canada

Eastern Canada

May

There is a strong
maximum 5 in southcentral BC (>3.7 days/
month), with a weaker
one (>0.5 days/month)
beginning to emerge in
western Alberta.

Values are generally low,
but show a maximum
near Maine (>0.34 days/
month), western Quebec
(>0.4 days/month) and
along the Nova Scotia
coast. The Nova Scotia
maximum is not physically
realistic, and is likely due
to the misreporting of ice
pellets as hail.

June

This pattern is similar to
May in western Canada,
but the maximum in
western Alberta (>1.76
days/month) is
intensifying and the B.C.
(>2.02 days/month) one
weakening slightly.
Values northwest of
Edmonton are slightly
lower than northwest of
Calgary.

Central Canada shows
similar patterns to May,
but with higher values
(>0.34 days/month
northeast of Regina).

The notable feature is the
maximum in western
Quebec (>0.42 days/
month). There is a weaker
maximum (>0.22 days/
month) east of Georgian
Bay.

July

Shows the same patterns
of a maximum in
east-central BC (>2.04
days/month), and in
western Alberta,
particularly just west of
Calgary (>1.24 days/
month).

Central Canada is not
showing the same high
values in southwestern
Saskatchewan and
Manitoba; the explanation
for this is not clear. There
are some significantly
high values however, of
>0.34 days/month north
of Regina.

Eastern Canada has an
interesting maximum
(>0.22 days/month)
northeast of Quebec City,
and a maximum in
western Quebec (>0.42
days/month).

Maximum: a local area of higher hail frequencies.

8

Table 2 (continued)
Month

Western Canada

Central Canada

Eastern Canada

August

Shows the same general
pattern as in the previous
months, but the B.C.
maximum (>1.3 days/
month) is becoming less
pronounced, while the
Alberta maximum (>1.5
days/month) has
intensified near Calgary,
but weakened northwest
of Edmonton (0.7 days/
month).

Several weak maxima
have appeared in south
Saskatchewan (>0.3 days/
month). Otherwise
patterns have low
gradients.

An interesting maximum
(>0.18 days/month) has
appeared just east of
Toronto. Other areas of
interest are western
Quebec (>0.16 days/
month) and near Maine
(>0.2 days/month).

September

The B.C. (>0.55 days/
month) and Alberta
(>0.3 days/month)
maximums are still
evident, but are
noticeably weaker.

A maximum (>0.28 days/
month) is still evident
southwest of Regina in
central Saskatchewan,
and also southeast of
Winnipeg.

The maximum in western
Quebec (>0.38 days/
month) persists, but the
maximum east of Toronto
is no longer evident; it
may be that the August
maximum is an artifact.

Southwestern
Saskatchewan has a
pronounced maximum of
>4.6 days/warm season,
with a secondary
maximum of >1.4 days/
warm season southwest
of Regina.

Eastern Canada shows a
pronounced maximum in
western Quebec (>1.2
days/warm season), and a
weaker one in
northwestern New
Brunswick (>0.8 days/
warm season).

Units in
these maps
are number
of hail days
over the
5 months
of MaySeptember.

9

Trends in Ontario:
Hail days from 1977-2006 averaged over the province of Ontario show a slight downward trend
that is not significant (Table 3 & Figure 7). As expected, year-to-year variability is high. This trend is
in contrast to previously documented trends in severe hail and tornadoes, as noted above. Note that
N varies from year-to-year and tends to be smaller in later years; mean hail day data is more reliable
when N is larger.

Table 3: Average hail frequencies per season (May-September) for the province
of Ontario. N = number of monthly hail observations, Mean = mean number of
hail days/station/month.
Year
N
Mean Standard Year
N
Mean Standard
Deviation
Deviation

1977

1,822 0.073 0.272 1992

1,414 0.058 0.256

1978

1,799 0.057 0.251 1993

1,321

0.077

0.298

1979

1,812 0.059 0.265 1994

1,256

0.101

0.324

1980

1,846 0.118 0.375 1995

1,166

0.046

0.227

1981

1,863 0.042 0.234 1996

1,098

0.101

0.348

1982

1,776 0.083 0.306 1997

1,032

0.043

0.215

1983

1,775 0.137 0.414 1998

1,038

0.113

0.365

1984

1,728 0.102 0.354 1999

1,012

0.074

0.278

1985

1,694 0.108 0.341 2000

937

0.083

0.303

1986

1,679 0.082 0.303 2001

894

0.043

0.228

1987

1,605 0.095 0.344 2002

832

0.069

0.288

1988

1,584 0.077 0.29 2003

800

0.072

0.279

1989

1,559 0.053 0.246 2004

735

0.074

0.303

1990

1,529 0.084 0.284 2005

719

0.055

0.24

1991

1,514 0.068 0.294 2006

732

0.067

0.263

10

Figure 7: Trends in hail frequency for Ontario. The vertical axis is mean number of
hail days/station for the months of May-September.

Ontario hail trend
0.16

y = 0.0006x + 1.2999

0.14

Mean hail frequency / station

R2 = 0.0506
0.12
0.1
0.08
0.06
0.04
0.02
0.0
1975

1980

1985

1990

1995

2000

2005

2010

A deluge of hail (some the size of golf balls) falls as a severe thunderstorm passes through
the town of Carstairs, Alberta. A small car slowly continues driving through the storm on
Highway 2A.

[CP PHOTO/Larry MacDougal]

11

Trends in Alberta:
The trend in hail days for the province of Alberta shows an increasing trend (Table 4 & Figure 9) that
is significant at the 95% confidence level, unlike Ontario. Year-to-year variability is also high.

Table 4: Average hail frequencies per year for the province of Alberta. N = number of
monthly hail observations, Mean = mean number of hail days/station/month.
Year
N
Mean Standard Year
N
Mean Standard
Deviation
Deviation

1977

0.242

1,104

0.635

1993

0.369

1,119

0.923

1978

0.214

1,111

0.560

1994

0.355

1,107

0.779

1979

0.254

1,118

0.643

1995

0.084

1,114

0.335

1980

0.277

1,114

0.697

1996

0.217

821

0.540

1981

0.172

1,167

0.624

1997

0.297

882

0.657

1982

0.195

1,209

0.540

1998

0.278

1,016

0.857

1983

0.358

1,214

0.906

1999

0.407

1,043

0.871

1984

0.430

1,224

0.897

2000

0.481

1,011

0.957

1985

0.402

1,225

0.942

2001

0.393

955

0.849

1986

0.351

1,239

0.914

2002

0.212

973

0.610

1987

0.329

1,218

0.824

2003

0.399

945

0.840

1988

0.203

1,235

0.560

2004

0.347

1,006

0.843

1989

0.388

1,243

0.873

2005

0.366

1,035

0.840

1990

0.279

1,203

0.731

2006

0.442

865

0.937

1991

0.359

1,205

0.886

2007

0.525

842

1.019

1992

0.355

1,187

0.777

12

Figure 8: Trends in hail frequency for Alberta. The vertical axis is mean number of
hail days per station for the months of May-September.

Alberta hail trend

0.6

y = 0.0048x â&#x20AC;&#x201C; 9.1909

Mean hail frequency / station

0.5

R2 = 0.1927

0.4

0.3

0.2

0.1

0.0
1975

1980

1985

1990

1995

2000

2005

2010

Calgary Stampeders defensive back Lin-J Shell checks out the hail after lightning and hail
hit Regina before pre-season CFL action against the Saskatchewan Roughriders on Friday
June 19, 2015.

[THE CANADIAN PRESS/Derek Mortensen]

13

Trends in Saskatchewan:
Hail trend data for Saskatchewan shows a decreasing trend that is statistically significant at the 95%
confidence level (Figure 9). The magnitude of the downward trend is much less than the increasing
trend in Alberta.
Table 5: Average hail frequencies per year for the province of Saskatchewan.
N = number of monthly hail observations, Mean = mean number of hail days/station.
Year
N
Mean Standard Year
N
Mean Standard
Deviation
Deviation

1977

0.251

713

0.565

1993

0.125

810

0.395

1978

0.239

706

0.564

1994

0.227

791

0.532

1979

0.194

698

0.489

1995

0.192

769

0.500

1980

0.168

710

0.414

1996

0.178

728

0.460

1981

0.293

743

0.675

1997

0.156

682

0.443

1982

0.184

728

0.460

1998

0.112

651

0.373

1983

0.178

727

0.445

1999

0.199

602

0.477

1984

0.135

766

0.407

2000

0.190

598

0.488

1985

0.146

805

0.403

2001

0.129

577

0.362

1986

0.197

822

0.480

2002

0.086

585

0.300

1987

0.164

834

0.453

2003

0.119

569

0.397

1988

0.142

827

0.395

2004

0.123

581

0.376

1989

0.208

833

0.538

2005

0.124

529

0.441

1990

0.179

828

0.484

2006

0.142

447

0.382

1991

0.190

821

0.461

2007

0.170

420

0.474

1992

0.159

816

0.436

14

Figure 9: Trends in hail frequency for Saskatchewan. The vertical axis is mean
number of hail days per station for the months of May-September.

Saskatchewan hail trend
0.35

y = 0.0028x + 5.7585

Mean hail frequency / station

0.3

R2 = 0.3278
0.25
0.2
0.15
0.1
0.05
0
1975

1980

1985

1990

1995

2000

2005

2010

Carol Bayntun of Markerville, Alberta shows damage to her plastic picnic table on Friday,
August 11, 2006. Damage is expected to be in the millions after hail from a powerful
thunderstorm punched holes in siding and broke windows in central Alberta earlier that
week, say county officials.

[CP PHOTO/Red Deer Advocate â&#x20AC;&#x201C; Jerry Gerling]

15

Trends in Manitoba:
Standard deviations of the hail frequencies show similar trends, as is evident by the strong correlation
between the two variables (Figure 11). The strong positive relationship between hail days and
standard deviation suggests that in low hail years, stations tend to be similar to each other with
few hail days, but in high hail years hail events are more unequally distributed, possibly because
some areas are more likely to experience a hail storm due to topographic, water body, and land use
differences.

Table 6: Average hail frequencies per year for the province of Manitoba. N = number
of monthly hail observations, Mean = mean number of hail days/station.
Year
N
Mean Standard Year
N
Mean Standard
Deviation
Deviation

1977

0.219

491

0.602

1993

0.144

589

0.411

1978

0.245

484

0.566

1994

0.235

604

0.591

1979

0.148

511

0.439

1995

0.149

585

0.405

1980

0.117

516

0.363

1996

0.129

578

0.416

1981

0.132

536

0.399

1997

0.135

547

0.577

1982

0.166

538

0.472

1998

0.088

511

0.317

1983

0.105

568

0.326

1999

0.183

487

0.456

1984

0.211

578

0.500

2000

0.155

471

0.414

1985

0.147

589

0.405

2001

0.140

454

0.381

1986

0.211

591

0.465

2002

0.117

431

0.344

1987

0.123

605

0.370

2003

0.112

435

0.340

1988

0.100

626

0.321

2004

0.110

404

0.347

1989

0.173

613

0.440

2005

0.127

355

0.390

1990

0.159

616

0.426

2006

0.113

305

0.368

1991

0.205

602

0.461

2007

0.121

284

0.382

1992

0.133

598

0.407

16

Figure 10: Trends in hail frequency for Manitoba. The vertical axis is mean number of
hail days per station for the months of May-September.

Manitoba hail trend
0.300

y = 0.0019x + 4.0157

Mean hail frequency / station

0.250

R2 = 0.1796
0.200
0.150
0.100
0.050
0.000
1975

1980

1985

1990

1995

2000

2005

2010

In front of a heavily damaged corn crop, Courtney Taylor holds left-over hail stones an hour
after a fast-moving severe thunderstorm with lightning, high winds, rain and hail hit the
High River area creating havoc with downed trees and destroying crops in some areas
southwest of High River, Alberta, August 14, 2012.

[THE CANADIAN PRESS IMAGES/Mike Sturk]

17

Figure 11: Relationship between mean number of hail days/station/month and
standard deviation (for the months of May, June, July, August and September) for
Ontario, Saskatchewan, Manitoba and Alberta.

Hail data (Ontario, Saskatchewan, Manitoba, Alberta)
1.2

1.0

y = 1.526x + 0.2785
R2 = 0.872

Standard deviation

0.8

0.6

0.4

0.2

0.0
0.0

0.1

0.2

0.3
Mean hail days

18

0.4

0.5

0.6

5. Conclusions and recommendations for future research

Hail is a significant hazard for Canada, causing damage to agriculture and property, but is very
unequally geographically and temporally distributed. Hail patterns identified in this analysis are similar
to those from Etkin and Brun (1999), but are more robust because of the larger number of years that
the data is based upon. A trend analysis shows no change in hail frequency for Ontario, in contrast to
other studies that have examined severe hail frequency and tornado frequency. Alberta, by contrast,
does show a significant increase in hail frequency during the period 1977 to 2007. Manitoba and
Saskatchewan show decreasing trends. Future research could examine in more detail which areas
exhibit increasing or decreasing hail frequencies, and how those seasons correlate with larger scale
climate drivers. This research would be constrained, however, by the lack of ongoing hail observations
by Environment Canada. Other datasets would have to be used, such as those created by radar and
satellite imagery.

Black clouds float ominously over the Calgary skyline just before a hail and heavy rain
storm hit Calgary and the Stampede grounds on Friday July 13, 2001. Extreme weather was
evident all across central Alberta.

David Etkin is an Associate Professor of Disaster and Emergency
Management at York University. He has contributed to several
national and international natural hazard projects including the
2nd U.S. national assessment of natural hazards, the IPCC, was
Principal Investigator of the Canadian National Assessment of
Natural Hazards and is Past President of the Canadian Risk and
Hazards Network. His areas of research include natural hazards,
risk, and ethics. He has over 80 publications including a textbook
on disaster theory and 6 edited volumes.
etkin@yorku.ca